Abstract:
Segmentation of anatomical regions of the brain is one of the
fundamental problems in medical image analysis. It is traditionally
solved by iso-surfacing or through the use of active contours/deformable
models on a gray-scale MRI data. In this paper we develop a technique
that uses anisotropic di usion properties of brain tissue available
from DT- MRI to segment out brain structures. We develop a computational
pipeline starting from raw diffusion tensor data, through computation
of invariant anisotropy measures to construction of geometric models of
the brain structures. This provides an environment for user-controlled
3D segmentation of DT-MRI datasets. We use level set approach to remove
noise from the data and to produce smooth, geometric models. We apply
our technique to DT-MRI data of a human subject and build models of the
isotropic and strongly anisotropic regions of the brain. Once geometric
models have been constructed they may be combined to study spatial
relationships and quantitatively analyzed to produce the volume and
surface area of the segmented regions.